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Record W2470395981 · doi:10.6000/1929-4409.2016.05.08

An Exploratory Study on How Democratic School Management Practices Affect the Dynamics of Violence in Schools

2016· article· en· W2470395981 on OpenAlexvenueno aff
Petro van der Merwe

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2016
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumContext (archaeology)PsychologyDynamics (music)Emotional intelligenceAffect (linguistics)DemocracyBehavior managementClassroom managementExploratory researchMathematics educationPedagogySocial psychologySociologyDevelopmental psychologyPolitical scienceSocial sciencePolitics

Abstract

fetched live from OpenAlex

The goal of teachers, schools and support staff is the optimal development of learners. This goal depends on effective school management, which in turn depends on an adequate curriculum and supporting teaching and learning environments. This paper is focused through a literature review on the dynamics of violence in South African schools and the need for democratic school management. The necessity of emotional intelligence teaching in the classroom and learner-centred teaching is shown. This study also investigated the coordinated and integrated management of positive learner behaviour, whole school development and management of a culture of positive behaviour. The establishment of a safe school depends on the management's interaction with the larger social and organisational context in which they find themselves. The study concludes that there is a correlation between Emotional Intelligence teaching methods and learners' behaviour. The use of Emotional Intelligence in the classroom can be regarded as the corrective aspect of discipline, thus, preventing inappropriate behaviour of learners. Recommendations and guidelines for the use of effective adjusted and alternative teaching methods to support school management in the use of emotional skills in the school context are provided.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.108
GPT teacher head0.418
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2016
Admission routes1
Has abstractyes

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